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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Computer Simulation of Proton Transport in Fuel Cell Membranes (Final Report)

This DOE-supported research grant focused on understanding the nature of proton transport in complex systems such as proton exchange membranes (PEMs). The most unique aspect of the research was the development and implementation of a novel multiscale reactive molecular dynamics (MS-RMD) methodology. In this approach, covalent bonds can dynamically break and form, allowing one to accurately treat the proton hopping process essential to capturing the physics of proton transport. The Voth group applied this method to proton exchange membrane systems, providing insight into their proton transport mechanism. They found that protons can diffuse most rapidly in the water-rich regions, but that protons actually spend so little time in such regions that transport along the hydrophobic – hydrophilic interface controls the membrane performance. In addition, the group worked to increase understanding of acidic solutions, developing methods for simulating and interpreting experimental infrared vibrational spectroscopy for excess protons (acidic solutions). The group also implemented novel tools for developing proton transport reactive MD models using a relative entropy minimization scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

UNESE Data Analysis-Disko Elm Gas Transport Characteristics (NA-22 Quarterly Report)

Recently published papers have demonstrated the impact of sorption mechanisms on gas transport in the subsurface following an underground nuclear explosion. To include sorption processes in the multi-physics models of noble gas production and transport, researchers have conducted experiments of gas sorption in geologic materials and calibrated a linear (Henry’s law) sorption model for all gases in various geologic materials. As a part of Task #4, we requested the experimental data from Sandia Laboratories and derived a dual-mode (Henry’s absorption and Langmuir adsorption) sorption model. The derived model better describes the nonlinear sorption mechanism with much higher fitness with the experimental data. When gas concentrations are relatively low (in the conditions of field experiments or underground nuclear explosions) compared to those used sorption experiments, the nonlinear sorption model can be approximated as a linear sorption model. The equivalent Henry’s constant, which equals the product of Langmuir capacity and affinity, is systematically higher than the literature values. Further analyses and comparison of sorption models are planned in FY2020 Q4 and the sorption model will be coupled in models of nonisothermal multiphase transport for studying noble gas detectability.

58 GEOSCIENCES↗
DOE OSTI2021

Microstructural-Level Fuel Performance Modeling of U-Mo Monolithic Fuel

As the physics that governs the microstructural evolution of nuclear fuel span various time and spatial scales, to fully understand the fuel behavior inevitably involves atomic to mesoscale resolution that can be difficult to determine experimentally. Microstructural-level modeling and simulations can be used to develop physics-based materials models that can provide physical understanding to inform fabrication process control, as well as a valuable feedback mechanism between post-irradiation examination (PIE) results and fabrication parameters. In accordance with the program schedule, the primary goals of the microstructure modeling effort are to: 1. Address critical microstructural questions and provide practical guidance to the fabricator via the fuel product specification; 2. Provide mechanistic inputs for the existing fuel performance code to improve its descriptive and predictive capability at the macroscopic scale. In fiscal year (FY)-21, the work scope consisted of six main facets: (1) the effect of carbides on fuel performance; (2) gas diffusivity in different phases; (3) integration of microstructural fuel performance modeling; (4) property degradation; (5) irradiation creep; and (6) historical analysis of microstructure data. Brief summaries of each are included below.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Brief Overview of Reactive Transport Codes Used in CO 2 Applications

Reactive transport modelling consists of computational and numerical models that describe the coupled physical, chemical, mechanical, and biological processes interacting with each other over a broad range of spatial and temporal scales. This report reviews some of the reactive transport codes available in the literature for carbon dioxide (CO 2 ) applications and can assist the scientific community with the applicability of each code to solve a specific problem. Approaches implemented in these codes can be categorized in three groups: (i) continuum-scale, (ii) pore-scale, and (iii) hybrid-scale approaches. Theoretical foundations, numerical implementations, and application examples using the models are described. In the continuum approach, flow and transport are formulated in terms of a representative control volume of the medium and coupled with geochemical reactions. The medium is characterized by bulk parameters such as porosity, permeability, or reactive surface area. In the pore-scale approach, each point of space in the pore network is occupied by either a fluid or solid phase. The pore-scale approach requires an exact knowledge of the spatial and temporal phase distribution. In the hybrid-scale approach, media are described by multiple characteristic length-scales, with some regions using a pore-scale approach while others used a continuum approach.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Energy-Efficient Self-Organization and Swarm Behavior in Active Matter

Living systems have the unique ability to form hierarchical assemblies, in which individual constituents can perform tasks cooperatively and emergently. Harnessing such properties is a long-standing challenge for the rational design of dynamic materials, that can respond to their environment, communicate with one another, and undergo a rapid, reversible, assembly through the transduction of energy. Recent developments in the design of smart and active colloidal building blocks have led to tremendous breakthroughs, with, for instance, the onset of synthetic photoactivated active assemblies. In this project, we develop a combined experimental, computational, theoretical and Machine Learning framework to shed light on the physical underpinnings of such assembly processes and program the assembly of smart active materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiwavelength Afterglow Analysis of GRB 221009A: Unveiling the Evolution of a Cooling Break in a Wind-like Medium

Gamma-ray bursts (GRBs) are the most energetic explosions in the Universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of GRB afterglow physics are still missing, including jet properties, the emission mechanism, and particle acceleration. Here, we present a study of the afterglow emission of GRB 221009A, the most energetic GRB ever observed. Using optical, X-ray, and gamma-ray data up to approximately 2 days after the trigger, we trace the evolution of the multiwavelength spectrum and the physical parameters behind the emission process. The broadband spectrum is consistent with the synchrotron emission emitted by relativistic electrons with its index of p = 2.29 ± 0.02. We identify a break energy at keV and an exponential cutoff at GeV in the observed multiwavelength spectrum. The break energy increases in time from $16.0^{+7.1}_{-4.9}$ keV at 0.65 days to $46.8^{+25.0}_{-15.5}$ keV at 1.68 days, favoring a stellar-wind-like profile of the circumburst medium with k = 2.4 ± 0.1 as in ρ(r) ∝ r -k . The high-energy attenuation at around 0.4 to 4 GeV is attributed to the maximum of the particle acceleration in the relativistic shock wave. This study confirms that the synchrotron process can explain the multiwavelength afterglow emission and its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Desorption lifetimes and activation energies influencing gas–surface interactions and multiphase chemical kinetics

Abstract. Adsorption and desorption of gases on liquid or solid substrates are involved in multiphase processes and heterogeneous chemical reactions. The desorption energy (Edes0), which depends on the intermolecular forces between adsorbate and substrate, determines the residence time of chemical species at interfaces. We show how Edes0 and temperature influence the net uptake or release of gas species, the rates of surface–bulk exchange and surface or bulk reactions, and the equilibration timescales of gas–particle partitioning. Using literature data, we derive a parameterization to estimate Edes0 for a wide range of chemical species based on the molecular mass, polarizability, and oxygen-to-carbon ratio of the desorbing species independent of substrate-specific properties, which is possible because of the dominant role of the desorbing species' properties. Correlations between Edes0 and the enthalpies of vaporization and solvation are rooted in molecular interactions. The relation between Edes0 and desorption kinetics reflects the key role of interfacial exchange in multiphase processes. For small molecules and semi-volatile organics (VOC, IVOC, SVOC), Edes0 values around 10–100 kJ mol−1 correspond to desorption lifetimes around nanoseconds to days at room temperature. Even higher values up to years are obtained at low temperatures and for low volatile organic compounds (LVOC, ELVOC/ULVOC) relevant for secondary organic aerosols (SOA). Implications are discussed for SOA formation, gas–particle partitioning, organic phase changes, and indoor surface chemistry. We expect these insights to advance the mechanistic and kinetic understanding of multiphase processes in atmospheric and environmental physical chemistry, aerosol science, materials science, and chemical engineering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Transforming Energy through Computational Excellence. Exascale Computing: Combustion; Simulating Effects of Fuel Injection Location in Supersonic Jet Engines

Computational tractable simulations using an adaptive-mesh-refinement solverfor compressible reacting flows help researchers understand how variations in fuel injection location within the supersonic flow cavity impacts combustion efficiency. By identifying the important physical determinants of the combustion processes, this study shows a promising pathway to improving flame stability and combustion efficiency, as well as reducing emissions.

adaptive mesh refinement↗
DOE OSTI2020

Sludge Removal: Success and Partnership with T Plant - 20510

U.S. DOE contractor CH2M Hill Plateau Remediation Company (CHPRC) has safely and efficiently managed the removal of highly radioactive sludge from the 105-K West Reactor fuel storage basin (105- KW Basin) near the Columbia River. In mid-September 2019, sludge retrieval was completed after 21 storage containers were safely loaded with highly radioactive sludge and transported approximately 13 miles from the 105-KW Basin to T Plant, located in the 200 West Area on the Central Plateau of the Hanford Site. Sludge removal represents the last major source term reduction necessary before the K West Reactor and fuel storage basin can transition to closure activities in preparation for final deactivation and demolition. The technical complexity of the sludge retrieval process, coupled with the challenging physical and radiological characteristics of the waste, necessitated a methodical and deliberate approach using unique design and operational solutions to safely conduct the work. This challenge was further complicated by the need to plan and conduct the work in two separate facilities, each subject to the controls and requirements of its own nuclear facility safety basis. The project overcame these challenges through creating an integrated team that actively engaged, communicated and coordinated each phase of the project to ensure successful completion. This paper will cover how these key elements of the Sludge Removal Project led to its success: - Integrating project planning and management; - Lessons learned from design and construction; - Preparing for a successful campaign (lessons learned from testing and start-up); - Lessons learned from operating in two separate facilities, transporting sludge containers on the Hanford Site and ensuring compliance with a range of regulatory and safety basis requirements. The establishment of an integrated group spanning two facilities and multiple organizations created a team capable of overcoming the challenges necessary to successfully plan and execute the sludge retrieval mission. Sharing lessons learned from this successful project can enhance the ability of teams across the DOE complex to successfully plan and execute complex projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Solvated Electrons Have Multiple Personalities in Molten Salts

P-Crosscut: Solvated Electrons Have Multiple Personalities in Molten Salts [EFRC – MSEE] Alejandro Ramos-Ballesteros;1 Hung H. Nguyen;2 Kazuhiro Iwamatsu;3 Santanu Roy;4 Vyacheslav Bryantsev;4 Michael E. Woods;1 Ruchi Gakhar;1 Phillip Halstenberg;4 Bobby Layne;5 Jay A. LaVerne;6 Claudio J. Margulis:2* and James F. Wishart5* 1Idaho National Laboratory; 2The University of Iowa; 3Hunter College; 4Oak Ridge National Laboratory; 5Brookhaven National Laboratory; 6University of Notre Dame Abstract: The solvated (eS–) is a powerful reducing agent and one of the primary products of molten salt radiolysis. In these extreme high-temperature environments, the eS– can initiate cascades of redox processes that significantly alter the physical and chemical properties of a molten salt, posing challenges for molten salt reactor (MSR) performance and longevity. Consequently, mastering the fundamental behavior of the eS– could enable the design of specific molten salt mixtures with tailored Lewis acidities for controlling the speciation and chemical reactivity of the eS–, thereby mitigating its overall impact on MSR technologies. Here, we use time-resolved electron pulse radiolysis techniques for determining chemical kinetics and transient absorption spectra, combined with ab initio molecular dynamics simulations to explore the influence of multivalent metal cations on the fundamental speciation and distribution of eS– coordination environments.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2025

Feedstock Variability: Differences Among Biomass Anatomical Fractions and Tissues

Corn stover is a versatile feedstock for producing biofuels and other sustainable products. However, its diverse chemical and physical attributes can hinder the processes that convert it into these compounds. The composition of corn stover varies according to factors including corn plant variety, cultivation conditions and maturity at harvest. In essence, corn stover consists of the polymers cellulose, hemicellulose and lignin. The structural core of plant cell walls consists of elongated chains of glucose molecules arranged in bundles of cellulose. Additionally, lower concentrations of hemicellulose, a complex carbohydrate composed of various sugars, form part of the matrix surrounding cellulose bundles. Lignin is a complex aromatic polymer that reinforces plant cell walls. Corn stover generally comprises 40%-50% cellulose, 20%-30% hemicellulose, and 20%-30% lignin. The ratios of these components vary based on the specific type of corn plant and the growth conditions. For example, corn plants cultivated in drought conditions exhibit higher lignin levels than those grown under optimal conditions. The compositional diversity resulting from growth conditions is most pronounced among the different anatomical fractions of the corn plant. Understanding and accommodating the variation among the anatomical fractions of corn stover can enhance the effective utilization of this resource.

09 BIOMASS FUELS↗
DOE OSTI2024

Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization

We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2024

Modeling and Scaling up of Membrane Modules Leveraging Dimensional Analysis

In the current analysis, we study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow. Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Model emulation and closure tests for (3+1)D relativistic heavy-ion collisions

In nuclear and particle physics, reconciling sophisticated simulations with experimental data is vital for understanding complex systems like the Quark Gluon Plasma (QGP) generated in heavy ion collisions. However, computational demands pose challenges, motivating using Gaussian Process emulators for efficient parameter extraction via Bayesian calibration. We conduct a comparative analysis of Gaussian Process emulators in heavy-ion physics to identify the most adept emulator for parameter extraction with minimal uncertainty. Furthermore, our study contributes to advancing computational techniques in heavy-ion physics, enhancing our ability to interpret experimental data and understand QGP properties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Partial-wave decomposition of the Keldysh ionization amplitude

We present an alternative way of calculating the Keldysh amplitude, i.e., the length-gauge form of the ionization amplitude in the strong-field approximation. The amplitude is evaluated exactly by expanding it in Fourier components and partial waves. Comparisons of the semianalytic model predictions with results of ab initio numerical simulations of the time-dependent Schrödinger equation for the interaction of electrons in short-range potentials with intense laser light yield excellent agreement, for wavelengths from the single photon to the multiphoton to the tunneling regime. Specifically, for ionization from initial states with higher angular momentum quantum number, e.g., p states, a significant improvement over predictions based on the popular saddle-point approximation is found. Furthermore, the current model rate allows for interpretation of the strong-field ionization process in terms of multiphoton absorption pathways and angular momentum selection rules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance

Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.

Biomolecular & subcellular processes↗